Environmental interlinked problems such as human-induced land cover change, water scarcity, loss in soil fertility, and anthropogenic climate change are expected to affect the viability of agriculture and increase food insecurity in many developing countries. Climate change is certainly the most serious of these challenges for the twenty-first century. The poorest regions of the world – tropical West Africa included – are the most vulnerable due to their high dependence on climate and weather sensitive activities such as agriculture, and the widespread poverty that limits the institutional and economic capacities to adapt to the new stresses brought about by climate change. Climate change is already acting negatively on the poor smallholders of tropical West Africa whose livelihoods dependent mainly on rain-fed agriculture that remains the cornerstone of the economy in the region. Adaptation of the agricultural systems to climate change effects is, therefore, crucial to secure the livelihoods of these rural communities. Since information is a key for decision-making, it is important to provide well-founded information on the magnitude of the impacts in order to design appropriate and sustainable adaptation strategies.
Considering the case of agricultural production in the Republic of Benin, this study aims at using large-scale climatic predictors to assess the potential impacts of past and future climate change on agricultural productivity at a country scale in West Africa. Climate signals from large-scale circulation were used because state-of-the art regional climate models (RCM) still do not perfectly resolve synoptic and mesoscale convective processes. It was hypothesised that in rain-fed systems with low investments in agricultural inputs, yield variations are widely governed by climatic factors. Starting with pineapple, a perennial fruit crops, the study further considered some annual crops such as cotton in the group of fibre crops, maize, sorghum and rice in the group of cereals, cowpeas and groundnuts belonging to the legume crops, and cassava and yams which are root and tuber crops. Thus the selected crops represented the three known groups of photosynthetic pathways (i.e. CAM, C3, and C4 plants).
In the study, use was made of the historical agricultural yield statistics for the Republic of Benin, observed precipitation and mean near-surface air temperature data from the Climatic Research Unit (CRU TS 3.1) and the corresponding variables simulated by the regional climate model (RCM) REMO. REMO RCM was driven at its boundaries by the global climate model ECHAM 5. Simulations with different greenhouse gas concentrations (SRES-A1B and B1 emission scenarios) and transient land cover change scenarios for present-day and future conditions were considered. The CRU data were submitted to empirical orthogonal functions analysis over the north hemispheric part of Africa to obtain large-scale observed climate predictors and associated consistent variability modes. REMO RCM data for the same region were projected on the derived climate patterns to get simulated climate predictors. By means of cross-validated Model Output Statistics (MOS) approach combined with Bayesian model averaging (BMA) techniques, the observed climate predictors and the crop predictand were further on used to derive robust statistical relationships. The robust statistical crop models perform well with high goodness-of-fit coefficients (e.g. for all combined crop models: 0.49 ≤ R2 ≤ 0.99; 0.28 ≤ Brier-Skill-Score ≤ 0.90).
Provided that REMO RCM captures the main features of the real African climate system and thus is able to reproduce its inter-annual variability, the time-independent statistical transfer functions were then used to translate future climate change signal from the simulated climate predictors into attainable crop yields/crop yield changes. The results confirm that precipitation and air temperature governed agricultural production in Benin in general, and particularly, pineapple yield variations are mainly influenced by temperature. Furthermore, the projected yield changes under future anthropogenic climate change during the first-half of the 21st century amount up to -12.5% for both maize and groundnuts, and -11%, -29%, -33% for pineapple, cassava, and cowpeas respectively. Meanwhile yield gain of up to +10% for sorghum and yams, +24% for cotton, and +39% for rice are expected. Over the time period 2001 – 2050, on average the future yield changes range between -3% and -13% under REMO SRES–B1 (GHG)+LCC, -2% and -11% under REMO SRES–A1B (GHG only),and -3% and -14% under REMO SRES–A1B (GHG)+LCC for pineapple, maize, sorghum, groundnuts, cowpeas and cassava. In the meantime for yams, cotton and rice, the average yield gains lie in interval of about +2% to +7% under REMO SRES–B1 (GHG)+LCC, +0.1% and +12% under REMO SRES–A1B (GHG only), and +3% and +10% under REMO SRES–A1B (GHG)+LCC. For sorghum, although the long-term average future yield depicts a reduction there are tendencies towards increasing yields in the future. The results also reveal that the increases in mean air temperature more than the changes in precipitation patterns are responsible for the projected yield changes. As well the results suggest that the reductions in pineapple yields cannot be attributed to the land cover/land use changes across sub-Saharan Africa. The production of groundnuts and in particular yams and cotton will profit from the on-going land use/land cover changes while the other crops will face detrimental effects.
Henceforth, policymakers should take effective measures to limit the on-going land degradation processes and all other anthropogenic actions responsible for temperature increase. Biotechnological improvement of the cultivated crop varieties towards development of set of seed varieties adapted to hotter and dry conditions should be included in the breeding pipeline programs. Amongst other solutions, application of appropriate climate-smart agricultural practices and conservation agriculture are also required to offset the negative impacts of climate change in agriculture.

The topic of the present study focuses on landslide susceptibility assessment in the Northern Vienna Forest by GIS-based, statistic-probabilistic and deterministic modelling. The study is based on two complementary approaches for integrated landslide susceptibility assessment, which is not limited to one single methodology and its inherent assumptions.
A statistic-probabilistic method is applied to the whole region of the Northern Vienna Forest. This regional model investigates the basic disposition for landslides under consideration of controlling factors, which are persistent and more or less constant over time. A deterministic method is applied on a larger scale in a sub-study site of the Hagenbach Valley. These detailed models aim to investigate the variable disposition as a function of substrate wetness, which is in turn dependent on meteorological conditions. A main aspect of the work is the development of various wetness scenarios, which consider short-term weather phenomena, like heavy or long-lasting rainfall, but which also investigate the influence of meteorological and climate conditions on slope stability, which may vary in mid-term and long-term.
Furthermore, the assessment of the effects of climate change on the disposition for landslides is a major aspect of the study. Hence, average changes in air temperature and precipitation as predicted by Regional Climate Models are incorporated into modelling. In this context, it is tested whether changes in substrate wetness and thus in slope stability can be identified and quantified as a consequence of changed climate conditions.
As further objective shallow slope movements are incorporated into disposition modelling. According to geomorphological and sedimentological studies, these quaternary sediments are essential for slope formation in the Vienna Forest. In general, it is assumed that landslides primarily occur in weathered flysch sandstones rich in marl. Field-based surveys, however, identified shallow landslide activity in the quaternary sediments covering the flysch bedrock in wide areas. Therefore, the influence of these sediments on slope dynamics is studied in the present work within GIS-based slope stability models.
The results of the statistic-probabilistic landslide susceptibility assessment provide information on the basic disposition of the Northern Vienna Forest for landslides. The resulting regional susceptibility map reveals that the Northern Zone, a tectonic unit in the north of the study area, has extensive areas with the highest degree of landslide susceptibility. In this overthrust area in transition to the Molasse Zone there are geological units which are highly susceptible to landslides. The “Wolfpassing Formation” and the “Calcareous Klippen” of the Northern Zone show significant landslide densities. These geological zones start in the north near St. Andrä-Wördern and continue in south-western direction along the ridges of Tulbinger Kogel, Klosterberg, Frauenberg, and Eichberg.
Statistical weighting carried out in the course of regional landslide susceptibility assessment provides information on the spatial relation between landslide processes and specific controlling factors. The modelling highlights the relevance of zones rich in clay within the flysch formations as controlling geofactor. The highest landslide susceptibility is calculated for the geological units, which contain layers of Gaultflysch rich in clay and shale. Furthermore, a close correlation between the distribution of landslides on the one hand and the spatial distribution of the fault system and nappe boundaries on the other hand is ascertained. Hence, the tectonic conditions can be seen as crucial controlling geofactor for landslide activity in the study area. In the proximity of drainage lines an increased landslide frequency is revealed. In combination with heavy rainfall, torrential discharge can occur in creeks and may cause instabilities in adjacent hillslopes. In addition, the model documents an enhancement of landslide susceptibility on north-west facing slopes. In comparison to meteorological data it is obvious that the north-west exposition corresponds to the prevailing wind direction of the study area. Therefore, north-west facing slopes might be exposed to enhanced advective rainfall amounts, which can increase substrate wetness and thus landslide susceptibility. The latter geofactors indicate the significance of meteorological and hydrological conditions for the occurrence of landslides in the study area.
As described above, the regional assessment is based on controlling factors that are persistent over a long period of time and can therefore be considered as constant. On the contrary, the large-scale, physically based deterministic modelling investigates the disposition for landslides under variable humidity conditions in the substrate. In conclusion it can be stated that the disposition for slope instability is strongly varying in dependence of the humidity conditions in the substrate. A heavy rainfall event causes a drastic reduction of stable areas by 23% compared to monthly average wetness conditions in summer (July). In summary the wetness scenarios demonstrate, that apart from short-term weather conditions, like long-lasting or heavy rainfall, the long-term-development of substrate moisture has impact on slope stability. The more persistent, seasonally fluctuating wetness conditions show measureable influence on slope stability: As a consequence of increased topographic wetness in the winter month February there is an increase of instable areas by 5% in comparison with the summer month July. The modelling further revealed that quaternary sediments are more moisture sensitive and the influence of changing wetness conditions is stronger in these layers than in the bedrock.
The results of modelling, which are based on climate change, indicate that a moderate change of slope stability on a monthly average is possible in comparison to the conditions of the climate normal period. An assumed average monthly temperature increase of 2°C in combination with a precipitation increase of 30% in the winter months lead to an augmentation of recharge of 7% in the model in comparison with the long-term average conditions. Due to this increased recharge, there is a slight increase of topographic wetness in the model. This wetness augmentation results in an extension of instable slope areas by 3% and a reduction of the stable slope areas proportional to this extension. This slightly increased instability reduces critical triggering thresholds for single rainfall events meaning that even lower precipitation amounts or intensities can cause instabilities.
In contrast to the winter months, the incorporation of forecasted climate change into the modelling reveals a reduction of instable slope areas in favour of stable areas in the summer scenario. The forecasted average air temperature increase of 2.5°C in combination with a reduction of the average monthly precipitation amount of 15% drastically decreases substrate moisture. Consequently, instable slope areas are reduced by 11% of the study area. This effect on slope stability in the model mainly results from the reduced monthly rainfall amounts, but also from increased evapotranspiration as a consequence of the increased air temperature causing reduced recharge amounts. However, in spite of the monthly decrease of precipitation amounts, precipitation intensities are probable to rise according to climate studies. In this context the results of the modelling indicate, that a drastic, short-term increase of landslide disposition due to heavy rainfall events has to be expected more frequently in summer.
The results of the complementary methods are then assembled. Based on this synthesis the following conclusion can be drawn: The regional landslide susceptibility assessment yields that hillslopes with an inclination of 26° to 31° are highly landslide prone. The physically based models indicate that in this slope gradient range the presence of quaternary sediments is of major importance for landslides. Therefore, it can be concluded that a considerable portion of known landslides mapped in flysch actually occurred in quaternary sediments.

The discontinuous mountain permafrost zone is characterized by its heterogeneous distribution of frozen ground and a small-scale variability of the ground thermal regime. Large parts of these areas are covered by glacial till and sediments that were exposed after the recession of the glaciers since the 19th century. As response to changed climatic conditions permafrost-affected areas will lose their ability as sediment storage and on the contrary, they will act as source areas for unconsolidated debris. Along with modified precipitation patterns the degradation of the discontinuous mountain permafrost zone will (temporarily)
increase its predisposition for mass movement processes and thus has to be monitored in a differentiated way.
Therefore, the spatio-temporal dynamics of frozen ground are assessed in this study based on results obtained in three glacier forefields in the Engadin (Swiss Alps) and at the Zugspitze (German Alps). Sophisticated techniques are required to uncover structural differences in the subsurface. Thus, the applicability of advanced geophysical methods is tested for alpine environments and proved by the good 3D-delineation of a permafrost body and by the detection of detailed processes in the active layer during snow melt. Electrical resistivity tomography (ERT) approaches (quasi-3D, daily monitoring) reveal
their capabilities to detect subsurface resistivity changes both, in space and time. Processes and changes in regard to liquid water content and ice content are observed to exist at short distances even though the active layer is not subject to a considerable thickening
over the past 7 years. The stability of the active layer is verified by borehole temperature data. No synchronous
trend is recognized in permafrost temperatures and together with multi-annual electrical resistivity data they indicate degradation and aggradation processes to occur at the same time. Different heat transfer mechanisms, especially during winter, are recognized by means of temperature sensors above, at, and beneath the surface. Based on surface and borehole temperature data the snow cover is assessed as the major controlling factor for the thermal regime on a local scale. Beyond that, the debris size of the substrate, which modifies the snow cover and regulates air exchange processes above the ground, plays a crucial role as an additional buffer layer. A fundamental control over the stability of local permafrost patches is attributed to the ice-rich transient layer at the base of the active layer. The refreezing of melt water in spring is illustrated with diurnal ERT monitoring data from glacier forefield Murtèl.
Based on these ERT and borehole temperature data a conceptual model of active layer processes between autumn and spring is developed. The latent heat that is inherent in the transient layer protects the permafrost beneath from additional energy input from the surface as long as the refreezing of melt water in spring prevails and sufficient ice is build up each spring. Permafrost sites without a transient layer show considerably higher
temperatures at their table and are more prone to degradation in the years and decades ahead. As main investigation area a glacier forefield beneath the summits of Piz Murtèl and Piz Corvatsch in the Swiss Engadin was chosen. It is located west of the well-known
rock glacier Murtèl. Here, a permafrost body inside and adjacent to the lateral moraine was investigated and could be delineated very well. In the surrounding glacier forefield no further indications of permafrost occurrence could be made. Geophysical data and temperature values from the surface and from a permafrost borehole were compared with long-term data from proximate glacier forefield Muragl (Engadin). Results from both
sites show a considerable stability of the active layer depth in summer while at the same time geophysical data demonstrate annual changes in the amount of liquid water content and ice content in the course of years.
A third investigation area is located in the German Alps. The Zugspitzplatt is a high mountain valley with considerably more precipitation and thicker snow cover compared to both Swiss sites. In close proximity to the present glacier and at a large talus slope beneath the summit crest ground ice could be observed. The high subsurface resistivity values and comparable data from existing studies at the Zugspitze may indicate the presence of sedimentary ice in the subsurface of the karstified Zugspitzplatt. Based on these complementary data from geophysical and temperature measurements as
well as geomorphological field mapping the development of permafrost in glacier forefields under climate change conditions is analyzed with cooperation partners from the SPCC project. Ground temperature simulations forced with long-term climatological data are modeled to assess future permafrost development in glacier forefield Murtèl. Results suggest that permafrost is stable as long as the ice-rich layer between the active layer and
the permafrost table exists. After a tipping point is reached, the disintegration of frozen ground starts to proceed rapidly from the top.

Considering its social, economic and natural conditions the Mediterranean Area is a highly vulnerable region by designated affections of climate change. Furthermore, its climatic characteristics are subordinated to high natural variability and are steered by various elements, leading to strong seasonal alterations. Additionally, General Circulation Models project compelling trends in specific climate variables within this region. These circumstances recommend this region for the scientific analyses conducted within this study. Based on the data of the CMIP3 database, the fundamental aim of this study is a detailed investigation of the total variability and the accompanied uncertainty, which superpose these trends, in the projections of temperature, precipitation and sea-level pressure by GCMs and their specific realizations. Special focus in the whole study is dedicated to the German model ECHAM5/MPI-OM. Following this ambition detailed trends and mean values are calculated and displayed for meaningful time periods and compared to reanalysis data of ERA40 and NCEP. To provide quantitative comparison the mentioned data are interpolated to a common 3x3° grid.
The total amount of variability is separated in its contributors by the application of an Analysis of Variance (ANOVA). For individual GCMs and their ensemble-members this is done with the application of a 1-way ANOVA, separating a treatment common to all ensemble-members and variability perturbating the signal given by different initial conditions. With the 2-way ANOVA the projections of numerous models and their realizations are analysed and the total amount of variability is separated into a common treatment effect, a linear bias between the models, an interaction coefficient and the residuals.
By doing this, the study is fulfilled in a very detailed approach, by considering yearly and seasonal variations in various reasonable time periods of 1961-2000 to match up with the reanalysis data, from 1961-2050 to provide a transient time period, 2001-2098 with exclusive regard on future simulations and 1901-2098 to comprise a time period of maximum length. The statistical analyses are conducted for regional-averages on the one hand and with respect to individual grid-cells on the other hand. For each of these applications the SRES scenarios of A1B, A2 and B1 are utilized. Furthermore, the spatial approach of the ANOVA is substituted by a temporal approach detecting the temporal development of individual variables. Additionally, an attempt is made to enlarge the signal by applying selected statistical methods.
In the detailed investigation it becomes evident, that the different parameters (i.e. length of temporal period, geographic location, climate variable, season, scenarios, models, etc…) have compelling impact on the results, either in enforcing or weakening them by different combinations. This holds on the one hand for the means and trends but also on the other hand for the contributions of the variabilities affecting the uncertainty and the signal. While temperature is a climate variable showing strong signals across these parameters, for precipitation mainly the noise comes to the fore, while for sea-level pressure a more differentiated result manifests. In turn, this recommends the distinguished consideration of the individual parameters in climate impact studies and processes in model generation, as the affecting parameters also provide information about the linkage within the system.
Finally, an investigation of extreme precipitation is conducted, implementing the variables of the total amount of heavy precipitation, the frequency of heavy-precipitation events, the percentage of this heavy precipitation to overall precipitation and the mean daily intensity from events of heavy precipitation. Each time heavy precipitation is defined to exceed the 95th percentile of overall precipitation. Consecutively mean values of these variables are displayed for ECHAM5/MPI-OM and the multi-model mean and climate sensitivities, by means of their difference between their average of the past period of 1981-2000 and the average of one of the future periods of 2046-2065 or 2081-2100. Following this investigation again an ANOVA is conducted providing a quantitative measurement of the severity of change of trends in heavy precipitation across several GCMs.
Besides it is a difficult task to account for extreme precipitation by GCMs, it is noteworthy that the investigated models differ highly in their projections, resulting partially in a more smoothed and meaningful multi-model mean. Seasonal alterations of the strength of this behaviour are quantitatively supported by the ANOVA.